A Better Way to Pretrain Deep Boltzmann Machines

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Geoffrey E. Hinton, Russ R. Salakhutdinov


We describe how the pre-training algorithm for Deep Boltzmann Machines (DBMs) is related to the pre-training algorithm for Deep Belief Networks and we show that under certain conditions, the pre-training procedure improves the variational lower bound of a two-hidden-layer DBM. Based on this analysis, we develop a different method of pre-training DBMs that distributes the modelling work more evenly over the hidden layers. Our results on the MNIST and NORB datasets demonstrate that the new pre-training algorithm allows us to learn better generative models.